Turnover numbers, also known as k cat values, are fundamental properties of enzymes. However, k cat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are k cat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate k vivo max , the observed maximal catalytic rate of an enzyme inside cells. Comparison with k cat values from Escherichia coli, yields a correlation of r 2 = 0.62 in log scale (p < 10 −10 ), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between k vivo max and k cat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a highthroughput method for extracting enzyme kinetic constants from omics data. (1-6). Many models of cellular metabolism include k cat values, the maximal turnover rates of enzymes, as key inputs to predict the behavior of metabolic pathways and networks (7-9). However, most values have never been measured experimentally. Escherichia coli is the most intensely biochemically characterized organism, but k cat values are available for only about 10% of its ≈ 2, 000 enzyme-reaction pairs (Dataset S1). Indeed, k cat values are missing for several central metabolic enzymes. The scarcity of kinetic data limits the scope of models and necessitates generic parameter assignments that significantly reduce the predictive power of cellular models.Even if a larger collection of k cat values was made available, their current use poses a major difficulty: k cat values are measured through in vitro enzyme assays, representing the initial rate of the reaction, i.e., full substrates saturation and negligible levels of products. Such assays may underrepresent factors like cellular metabolite concentrations, thermodynamic constraints, posttranslational modifications, chaperones, cellular crowding, and activating and inhibiting molecules, which can substantially alter enzyme kinetics in vivo. These omissions call into question the relevance of k cat measurements in vivo (10-12). Furthermore, an effort to measure a large number of k cat values under in vivo-like conditions presents a daunting challenge, given how many unknown biochemical factors might be involved.Several studies grapple with missing k cat values by sampling from the distribution of k cat values measured in vitro or by using measurements of the same enzyme from related species (13-16). These approximations systematically ignore any errors resulting from the differences between in vitro and in vivo environments. Approximations of this sort may also introduce significant errors, as k cat values can deviate by orders of magnitude between isozymes in the same organism as well a...

Since the publication of Leonor Michaelis and Maude Menten's paper on the reaction kinetics of the enzyme invertase in 1913, molecular biology has evolved tremendously. New measurement techniques allow in vivo characterization of the whole genome, proteome or transcriptome of cells, whereas the classical enzyme essay only allows determination of the two Michaelis–Menten parameters V and Km. Nevertheless, Michaelis–Menten kinetics are still commonly used, not only in the in vitro context of enzyme characterization but also as a rate law for enzymatic reactions in larger biochemical reaction networks. In this review, we give an overview of the historical development of kinetic rate laws originating from Michaelis–Menten kinetics over the past 100 years. Furthermore, we briefly summarize the experimental techniques used for the characterization of enzymes, and discuss web resources that systematically store kinetic parameters and related information. Finally, describe the novel opportunities that arise from using these data in dynamic mathematical modeling. In this framework, traditional in vitro approaches may be combined with modern genome‐scale measurements to foster thorough understanding of the underlying complex mechanisms.

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In a porcine model these early results could show that all of the used CT perfusion parameters allowed discrimination of necrosis from vital tissue after RFA at high levels of significance. In addition, the parameters EquivBV and FE that give an estimate of the tissue blood volume and the permeability, were able to precisely discern different zones also seen macroscopically. From this data CT perfusion analysis could be precise tool for measurement and visualization of ablated liver lesions and for immediate detection of incomplete ablation areas.

Lipid metabolism is essential for all major cell functions and has recently gained increasing attention in research and health studies. However, mathematical modeling by means of classical approaches such as stoichiometric networks and ordinary differential equation systems has not yet provided satisfactory insights, due to the complexity of lipid metabolism characterized by many different species with only slight differences and by promiscuous multifunctional enzymes. Here, we present an object-oriented stochastic model approach as a way to cope with the complex lipid metabolic network. While all lipid species are treated objects in the model, they can be modified by the respective converting reactions based on reaction rules, a hybrid method that integrates benefits of agent-based and classical stochastic simulation. This approach allows to follow the dynamics of all lipid species with different fatty acids, different degrees of saturation and different headgroups over time and to analyze the effect of parameter changes, potential mutations in the catalyzing enzymes or provision of different precursors. Applied to yeast metabolism during one cell cycle period, we could analyze the distribution of all lipids to the various membranes in time-dependent manner. The presented approach allows to efficiently treat the complexity of cellular lipid metabolism and to derive conclusions on the time- and location-dependent distributions of lipid species and their properties such as saturation. It is widely applicable, easily extendable and will provide further insights in healthy and diseased states of cell metabolism.